X, Y = data_to_run['x'], data_to_run['y']
print('\n\n')
print('BATCHED X SHAPE: {}'.format(X.shape))
for ii in range(noutputs):
print('BATCHED Y {} SHAPE: {}'.format(ii, Y[ii].shape))
print('TRAINING, WATCH THE GPU UTILIZATION SPIKE ON ALL GPUS')
loss_rpn = model_rpn.train_on_batch(X, Y)
P_rpn = model_rpn.predict_on_batch(X)
print('\n\n ==================== WILL CRASH SOON. YOU FIX IT '
'=======================\n\n')
R = roi_helpers.rpn_to_roi(P_rpn[0], P_rpn[1], C, K.image_dim_ordering(
), use_regr=True, overlap_thresh=0.7, max_boxes=300)
# note: calc_iou converts from (x1,y1,x2,y2) to (x,y,w,h) format
X2, Y1, Y2, IouS = roi_helpers.calc_iou(
R, img_data, C, class_mapping)
if X2 is None:
rpn_accuracy_rpn_monitor.append(0)
rpn_accuracy_for_epoch.append(0)
continue
neg_samples = np.where(Y1[0, :, -1] == 1)
pos_samples = np.where(Y1[0, :, -1] == 0)
if len(neg_samples) > 0:
neg_samples = neg_samples[0]
else:
neg_samples = []
if len(pos_samples) > 0:
def train_kitti():
df = pd.DataFrame([], columns=[
'epoch',
'loss_rpn_cls',
'loss_rpn_regr',
'loss_class_cls',
'loss_class_regr',
'class_acc',
'curr_loss'
])
# config for data argument
cfg = config.Config()
cfg.use_horizontal_flips = True
cfg.use_vertical_flips = True
cfg.rot_90 = True
cfg.num_rois = 32
cfg.base_net_weights = os.path.join('./model/', nn.get_weight_path())
# TODO: the only file should to be change for other data to train
cfg.model_path = './model_trained/resnet50_weights_tf_dim_ordering_tf_kernels.h5'
cfg.simple_label_file = '../../dataset/youcook2/YouCook2/yc_bbox.txt'
if not (os.path.exists('all_images.pkl') and os.path.exists('classes_count.pkl') and os.path.exists('class_mapping.pkl')):
all_images, classes_count, classes_count = get_data(cfg.simple_label_file)
with open('all_images.pkl', 'wb') as f:
pickle.dump(all_images, f)
with open('classes_count.pkl', 'wb') as f:
pickle.dump(classes_count, f)
with open('class_mapping.pkl', 'wb') as f:
pickle.dump(classes_count, f)
else:
with open('all_images.pkl', 'rb') as f:
all_images = pickle.load(f)
with open('classes_count.pkl', 'rb') as f:
classes_count = pickle.load(f)
with open('class_mapping.pkl', 'rb') as f:
class_mapping = pickle.load(f)
if 'bg' not in classes_count:
classes_count['bg'] = 0
class_mapping['bg'] = len(class_mapping)
cfg.class_mapping = class_mapping
with open(cfg.config_save_file, 'wb') as config_f:
pickle.dump(cfg, config_f)
print('Config has been written to {}, and can be loaded when testing to ensure correct results'.format(
cfg.config_save_file))
inv_map = {v: k for k, v in class_mapping.items()}
print('Training images per class:')
pprint.pprint(classes_count)
print('Num classes (including bg) = {}'.format(len(classes_count)))
random.shuffle(all_images)
num_imgs = len(all_images)
train_imgs = [s for s in all_images if s['imageset'] == 'trainval']
val_imgs = [s for s in all_images if s['imageset'] == 'test']
print('Num train samples {}'.format(len(train_imgs)))
print('Num val samples {}'.format(len(val_imgs)))
data_gen_train = data_generators.get_anchor_gt(train_imgs, classes_count, cfg, nn.get_img_output_length,
K.image_dim_ordering(), mode='train')
data_gen_val = data_generators.get_anchor_gt(val_imgs, classes_count, cfg, nn.get_img_output_length,
K.image_dim_ordering(), mode='val')
if K.image_dim_ordering() == 'th':
input_shape_img = (3, None, None)
else:
input_shape_img = (None, None, 3)
img_input = Input(shape=input_shape_img)
roi_input = Input(shape=(None, 4))
# define the base network (resnet here, can be VGG, Inception, etc)
shared_layers = nn.nn_base(img_input, trainable=True)
# define the RPN, built on the base layers
num_anchors = len(cfg.anchor_box_scales) * len(cfg.anchor_box_ratios)
rpn = nn.rpn(shared_layers, num_anchors)
classifier = nn.classifier(shared_layers, roi_input, cfg.num_rois, nb_classes=len(classes_count), trainable=True)
model_rpn = Model(img_input, rpn[:2])
model_classifier = Model([img_input, roi_input], classifier)
# this is a model that holds both the RPN and the classifier, used to load/save weights for the models
model_all = Model([img_input, roi_input], rpn[:2] + classifier)
try:
print('loading weights from {}'.format(cfg.base_net_weights))
model_rpn.load_weights(cfg.model_path, by_name=True)
model_classifier.load_weights(cfg.model_path, by_name=True)
except Exception as e:
print(e)
print('Could not load pretrained model weights. Weights can be found in the keras application folder '
'https://github.com/fchollet/keras/tree/master/keras/applications')
optimizer = Adam(lr=1e-5)
optimizer_classifier = Adam(lr=1e-5)
model_rpn.compile(optimizer=optimizer,
loss=[losses_fn.rpn_loss_cls(num_anchors), losses_fn.rpn_loss_regr(num_anchors)])
model_classifier.compile(optimizer=optimizer_classifier,
loss=[losses_fn.class_loss_cls, losses_fn.class_loss_regr(len(classes_count) - 1)],
metrics={'dense_class_{}'.format(len(classes_count)): 'accuracy'})
model_all.compile(optimizer='sgd', loss='mae')
epoch_length = 1000
num_epochs = int(cfg.num_epochs)
iter_num = 0
losses = np.zeros((epoch_length, 5))
rpn_accuracy_rpn_monitor = []
rpn_accuracy_for_epoch = []
start_time = time.time()
best_loss = np.Inf
class_mapping_inv = {v: k for k, v in class_mapping.items()}
print('Starting training')
vis = True
for epoch_num in range(num_epochs):
progbar = generic_utils.Progbar(epoch_length)
print('Epoch {}/{}'.format(epoch_num + 1, num_epochs))
while True:
try:
if len(rpn_accuracy_rpn_monitor) == epoch_length and cfg.verbose:
mean_overlapping_bboxes = float(sum(rpn_accuracy_rpn_monitor)) / len(rpn_accuracy_rpn_monitor)
rpn_accuracy_rpn_monitor = []
print(
'Average number of overlapping bounding boxes from RPN = {} for {} previous iterations'.format(
mean_overlapping_bboxes, epoch_length))
if mean_overlapping_bboxes == 0:
print('RPN is not producing bounding boxes that overlap'
' the ground truth boxes. Check RPN settings or keep training.')
X, Y, img_data = next(data_gen_train)
loss_rpn = model_rpn.train_on_batch(X, Y)
P_rpn = model_rpn.predict_on_batch(X)
result = roi_helpers.rpn_to_roi(P_rpn[0], P_rpn[1], cfg, K.image_dim_ordering(), use_regr=True,
overlap_thresh=0.7,
max_boxes=300)
# note: calc_iou converts from (x1,y1,x2,y2) to (x,y,w,h) format
X2, Y1, Y2, IouS = roi_helpers.calc_iou(result, img_data, cfg, class_mapping)
if X2 is None:
rpn_accuracy_rpn_monitor.append(0)
rpn_accuracy_for_epoch.append(0)
continue
neg_samples = np.where(Y1[0, :, -1] == 1)
pos_samples = np.where(Y1[0, :, -1] == 0)
if len(neg_samples) > 0:
neg_samples = neg_samples[0]
else:
neg_samples = []
if len(pos_samples) > 0:
pos_samples = pos_samples[0]
else:
pos_samples = []
rpn_accuracy_rpn_monitor.append(len(pos_samples))
rpn_accuracy_for_epoch.append((len(pos_samples)))
if cfg.num_rois > 1:
if len(pos_samples) < cfg.num_rois // 2:
selected_pos_samples = pos_samples.tolist()
else:
selected_pos_samples = np.random.choice(pos_samples, cfg.num_rois // 2, replace=False).tolist()
try:
selected_neg_samples = np.random.choice(neg_samples, cfg.num_rois - len(selected_pos_samples),
replace=False).tolist()
except:
selected_neg_samples = np.random.choice(neg_samples, cfg.num_rois - len(selected_pos_samples),
replace=True).tolist()
sel_samples = selected_pos_samples + selected_neg_samples
else:
# in the extreme case where num_rois = 1, we pick a random pos or neg sample
selected_pos_samples = pos_samples.tolist()
selected_neg_samples = neg_samples.tolist()
if np.random.randint(0, 2):
sel_samples = random.choice(neg_samples)
else:
sel_samples = random.choice(pos_samples)
loss_class = model_classifier.train_on_batch([X, X2[:, sel_samples, :]],
[Y1[:, sel_samples, :], Y2[:, sel_samples, :]])
losses[iter_num, 0] = loss_rpn[1]
losses[iter_num, 1] = loss_rpn[2]
losses[iter_num, 2] = loss_class[1]
losses[iter_num, 3] = loss_class[2]
losses[iter_num, 4] = loss_class[3]
iter_num += 1
progbar.update(iter_num,
[('rpn_cls', np.mean(losses[:iter_num, 0])), ('rpn_regr', np.mean(losses[:iter_num, 1])),
('detector_cls', np.mean(losses[:iter_num, 2])),
('detector_regr', np.mean(losses[:iter_num, 3]))])
if iter_num == epoch_length:
loss_rpn_cls = np.mean(losses[:, 0])
loss_rpn_regr = np.mean(losses[:, 1])
loss_class_cls = np.mean(losses[:, 2])
loss_class_regr = np.mean(losses[:, 3])
class_acc = np.mean(losses[:, 4])
mean_overlapping_bboxes = float(sum(rpn_accuracy_for_epoch)) / len(rpn_accuracy_for_epoch)
rpn_accuracy_for_epoch = []
if cfg.verbose:
print('Mean number of bounding boxes from RPN overlapping ground truth boxes: {}'.format(
mean_overlapping_bboxes))
print('Classifier accuracy for bounding boxes from RPN: {}'.format(class_acc))
print('Loss RPN classifier: {}'.format(loss_rpn_cls))
print('Loss RPN regression: {}'.format(loss_rpn_regr))
print('Loss Detector classifier: {}'.format(loss_class_cls))
print('Loss Detector regression: {}'.format(loss_class_regr))
print('Elapsed time: {}'.format(time.time() - start_time))
curr_loss = loss_rpn_cls + loss_rpn_regr + loss_class_cls + loss_class_regr
iter_num = 0
start_time = time.time()
result = pd.DataFrame([[
epoch_num + 1,
loss_rpn_cls,
loss_rpn_regr,
loss_class_cls,
loss_class_regr,
class_acc,
curr_loss
]], columns=[
'epoch',
'loss_rpn_cls',
'loss_rpn_regr',
'loss_class_cls',
'loss_class_regr',
'class_acc',
'curr_loss'
])
df = df.append(result)
if os.path.exists('result.csv'):
os.remove('result.csv')
df.to_csv("result.csv", index=False, encoding="utf-8", mode='a', header=False)
if curr_loss < best_loss:
if cfg.verbose:
print('Total loss decreased from {} to {}, saving weights'.format(best_loss, curr_loss))
best_loss = curr_loss
model_all.save_weights(cfg.model_path)
break
except Exception as e:
print('Exception: {}'.format(e))
# save model
model_all.save_weights(cfg.model_path)
continue
print('Training complete, exiting.')
X, Y, img_data = next(data_gen_train)
loss_rpn = model_rpn.train_on_batch(X, Y)
P_rpn = model_rpn.predict_on_batch(X)
R = roi_helpers.rpn_to_roi(P_rpn[0],
P_rpn[1],
C,
K.image_dim_ordering(),
use_regr=True,
overlap_thresh=0.7,
max_boxes=300)
# note: calc_iou converts from (x1,y1,x2,y2) to (x,y,w,h) format
print(class_mapping)
X2, Y1, Y2, IouS = roi_helpers.calc_iou(R, img_data, C,
class_mapping)
if X2 is None:
rpn_accuracy_rpn_monitor.append(0)
rpn_accuracy_for_epoch.append(0)
continue
neg_samples = np.where(Y1[0, :, -1] == 1)
pos_samples = np.where(Y1[0, :, -1] == 0)
if len(neg_samples) > 0:
neg_samples = neg_samples[0]
else:
neg_samples = []
if len(pos_samples) > 0:
X, Y, img_data = next(data_gen_train)
counter += 1
print(" img {}: {}".format(str(counter), img_data['filepath']))
loss_rpn = model_rpn.train_on_batch(X, Y)
#print("train done")
P_rpn = model_rpn.predict_on_batch(X) # [x_class, x_regr]
#print("predict done")
# R = bboxes (300 ,4)
R = roi_helpers.rpn_to_roi(P_rpn[0], P_rpn[1], C, K.image_dim_ordering(), use_regr=True, overlap_thresh=0.7, max_boxes=300)
# note: calc_iou converts from (x1,y1,x2,y2) to (x,y,w,h) format
# pred_ROIs = arr of predicted RoI ([x1, y1, w, h])
# pred_cls = arr of one-hot class label
# pred_regr = arr of regr labels and coords
# IoU = list of IoU
pred_ROIs, pred_cls, pred_regr, IouS = roi_helpers.calc_iou(R, img_data, C, class_mapping)
if pred_ROIs is None:
rpn_accuracy_rpn_monitor.append(0)
rpn_accuracy_for_epoch.append(0)
continue
neg_samples = np.where(pred_cls[0, :, -1] == 1) # is background
pos_samples = np.where(pred_cls[0, :, -1] == 0) # is not background
if len(neg_samples) > 0:
neg_samples = neg_samples[0] # all neg samples
else:
neg_samples = []
if len(pos_samples) > 0:
pos_samples = pos_samples[0] # all pos samples
Valid_rpn = model_rpn.predict_on_batch(X_valid)
## get most 350 predicted anchor boxes for training Classification
R = roi_helpers.rpn_to_roi(P_rpn[0],
P_rpn[1],
C,
use_reg=True,
overlap_thresh=0.7,
max_boxes=350)
R_valid = roi_helpers.rpn_to_roi(Valid_rpn[0],
Valid_rpn[1],
C,
use_reg=True,
overlap_thresh=0.7,
max_boxes=350)
## calc_iou converts from (x1,y1,x2,y2) to (x,y,w,h) format
X2, Y1, Y2, IouS = roi_helpers.calc_iou(R, img_data, C,
class_mapping)
X2_valid, Y1_valid, Y2_valid, IouS_valid = roi_helpers.calc_iou(
R_valid, img_data_valid, C, class_mapping)
if X2 is None:
rpn_accuracy_rpn_monitor.append(0)
rpn_accuracy_for_epoch.append(0)
continue
## last one is 'bg', so check neg or pos
neg_samples = np.where(Y1[0, :, -1] == 1)
pos_samples = np.where(Y1[0, :, -1] == 0)
pos_samples_valid = np.where(Y1_valid[0, :, -1] == 0)
neg_samples_valid = np.where(Y1_valid[0, :, -1] == 1)
num_pos_valid = len(pos_samples_valid[0])
def train_model(dataset_directory: str,
model_name: str,
delete_and_recreate_dataset_directory: bool,
configuration_name: str,
output_weight_path: str,
configuration_filename: str,
number_of_epochs: int,
early_stopping: int,
learning_rate_reduction_patience: int,
learning_rate_reduction_factor: float,
non_max_suppression_overlap_threshold: float,
non_max_suppression_max_boxes: int,
input_weight_path: str = None):
muscima_pp_raw_dataset_directory = os.path.join(dataset_directory,
"muscima_pp_raw")
muscima_image_directory = os.path.join(dataset_directory,
"cvcmuscima_staff_removal")
muscima_cropped_directory = os.path.join(dataset_directory,
"muscima_pp_cropped_images")
if not dataset_directory: # if filename is not given
parser.error(
'Error: path to training data must be specified. Pass --path to command line'
)
network = NetworkFactory.get_network_by_name(model_name)
try:
training_images, validation_images, test_images, classes_count, class_mapping = \
get_data(dataset_directory, os.path.join(dataset_directory, "Annotations.txt"))
data_loaded = True
except:
print(
"Could not load dataset. Automatically downloading and recreating dataset."
)
data_loaded = False
delete_and_recreate_dataset_directory = True
if delete_and_recreate_dataset_directory:
print("Deleting dataset directory {0}".format(dataset_directory))
if os.path.exists(dataset_directory):
shutil.rmtree(dataset_directory)
downloader = MuscimaPlusPlusDatasetDownloader(
muscima_pp_raw_dataset_directory)
downloader.download_and_extract_dataset()
downloader = CvcMuscimaDatasetDownloader(
muscima_image_directory, CvcMuscimaDataset.StaffRemoval)
downloader.download_and_extract_dataset()
delete_unused_images(muscima_image_directory)
inverter = ImageInverter()
# We would like to work with black-on-white images instead of white-on-black images
inverter.invert_images(muscima_image_directory, "*.png")
shutil.copy("Staff-Vertical-Positions.txt", dataset_directory)
cut_images(
muscima_image_directory,
os.path.join(dataset_directory, "Staff-Vertical-Positions.txt"),
muscima_cropped_directory, muscima_pp_raw_dataset_directory,
os.path.join(dataset_directory, "Annotations.txt"),
os.path.join(dataset_directory, "Annotations"))
dataset_splitter = DatasetSplitter(muscima_cropped_directory,
dataset_directory)
dataset_splitter.split_images_into_training_validation_and_test_set()
# pass the settings from the command line, and persist them in the config object
C = ConfigurationFactory.get_configuration_by_name(configuration_name)
C.model_path = output_weight_path
start_time = time.time()
if not data_loaded:
training_images, validation_images, test_images, classes_count, class_mapping = \
get_data(dataset_directory, os.path.join(dataset_directory, "Annotations.txt"))
if 'bg' not in classes_count:
classes_count['bg'] = 0
class_mapping['bg'] = len(class_mapping)
C.class_mapping = class_mapping
s = ""
object_classes = list(class_mapping.keys())
object_classes.sort()
i = 1
for object_class in object_classes:
if object_class is "bg":
continue
s += "item {\n"
s += " id: {0}\n".format(i)
s += " name: '{0}'\n".format(object_class)
s += "}\n\n"
i += 1
with open("mapping.txt", "w") as mapping:
mapping.write(s)
# inv_map = {v: k for k, v in class_mapping.items()}
print('Training images per class:')
pprint.pprint(classes_count)
print('Num classes (including bg) = {}'.format(len(classes_count)))
print(
'Hyperparameters: {0} RoIs generated per run with {1} boxes remaining from non-max suppression and using '
'non-max suppression threshold of {2:.2f}'.format(
C.num_rois, non_max_suppression_max_boxes,
non_max_suppression_overlap_threshold))
config_output_filename = configuration_filename
with open(config_output_filename, 'wb') as config_f:
pickle.dump(C, config_f)
print(
'Config has been written to {}, and can be loaded when testing to ensure correct results'
.format(config_output_filename))
random.seed(1)
random.shuffle(training_images)
print('Number of training samples: {}'.format(len(training_images)))
print('Number of validation samples: {}'.format(len(validation_images)))
print('Number of test samples: {}'.format(len(test_images)))
if not use_fast_data_generators:
print("Using standard data_generator")
data_gen_train = data_generators.get_anchor_gt(
training_images,
classes_count,
C,
network.get_img_output_length,
mode='train')
data_gen_val = data_generators.get_anchor_gt(
validation_images,
classes_count,
C,
network.get_img_output_length,
mode='val')
else:
print("Using fast data_generator")
data_gen_train = data_generators_fast.get_anchor_gt(
training_images,
classes_count,
C,
network.get_img_output_length,
mode='train')
data_gen_val = data_generators_fast.get_anchor_gt(
validation_images,
classes_count,
C,
network.get_img_output_length,
mode='val')
input_shape_img = (None, None, 3)
img_input = Input(shape=input_shape_img)
roi_input = Input(shape=(None, 4))
# define the base network (resnet here, can be VGG, Inception, etc)
shared_layers = network.nn_base(img_input, trainable=True)
# define the RPN, built on the base layers
num_anchors = len(C.anchor_box_scales) * len(C.anchor_box_ratios)
rpn = network.rpn(shared_layers, num_anchors)
classifier = network.classifier(shared_layers,
roi_input,
C.num_rois,
nb_classes=len(classes_count),
trainable=True)
model_rpn = Model(img_input, rpn[:2])
model_classifier = Model([img_input, roi_input], classifier)
# this is a model that holds both the RPN and the classifier, used to load/save weights for the models
model_all = Model([img_input, roi_input], rpn[:2] + classifier)
start_of_training = datetime.date.today()
tensorboard_callback = TensorBoard(log_dir="./logs/{0}_{1}/".format(
start_of_training, configuration_name))
tensorboard_callback.set_model(model_all)
try:
print('Loading weights from {0}'.format(input_weight_path))
model_rpn.load_weights(input_weight_path, by_name=True)
model_classifier.load_weights(input_weight_path, by_name=True)
except:
print(
'Could not load pretrained model weights. Weights can be found in the keras application folder \
https://github.com/fchollet/keras/tree/master/keras/applications')
optimizer = Adadelta()
optimizer_classifier = Adadelta()
model_rpn.compile(optimizer=optimizer,
loss=[
faster_rcnn_losses.rpn_loss_cls(num_anchors),
faster_rcnn_losses.rpn_loss_regr(num_anchors)
])
model_classifier.compile(
optimizer=optimizer_classifier,
loss=[
faster_rcnn_losses.class_loss_cls,
faster_rcnn_losses.class_loss_regr(len(classes_count) - 1)
],
metrics={'dense_class_{}'.format(len(classes_count)): 'accuracy'})
model_all.compile(optimizer=Adadelta(), loss='mae')
epoch_length = 1000
validation_epoch_length = len(validation_images)
validation_interval = 1
losses = np.zeros((epoch_length, 5))
losses_val = np.zeros((validation_epoch_length, 5))
rpn_accuracy_rpn_monitor = []
rpn_accuracy_for_epoch = []
best_loss_training = np.inf
best_loss_epoch = 0
best_total_loss_validation = np.Inf
best_loss_rpn_cls = np.inf
best_loss_rpn_regr = np.inf
best_loss_class_cls = np.inf
best_loss_class_regr = np.inf
best_class_acc = 0.0
model_classifier.summary()
print(C.summary())
print('Starting training')
train_names = [
'train_loss_rpn_cls', 'train_loss_rpn_reg', 'train_loss_class_cls',
'train_loss_class_reg', 'train_total_loss', 'train_acc'
]
val_names = [
'val_loss_rpn_cls', 'val_loss_rpn_reg', 'val_loss_class_cls',
'val_loss_class_reg', 'val_total_loss', 'val_acc'
]
epochs_without_improvement = 0
for epoch_num in range(number_of_epochs):
progbar = generic_utils.Progbar(epoch_length)
print('Epoch {}/{}'.format(epoch_num + 1, number_of_epochs))
for iter_num in range(epoch_length):
try:
if len(rpn_accuracy_rpn_monitor) == epoch_length and C.verbose:
mean_overlapping_bboxes = float(
sum(rpn_accuracy_rpn_monitor)) / len(
rpn_accuracy_rpn_monitor)
rpn_accuracy_rpn_monitor = []
print(
'\nAverage number of overlapping bounding boxes from RPN = {} for {} previous iterations'
.format(mean_overlapping_bboxes, epoch_length))
if mean_overlapping_bboxes == 0:
print(
'RPN is not producing bounding boxes that overlap the ground truth boxes. Check RPN settings or keep training.'
)
X, Y, img_data = next(data_gen_train)
loss_rpn = model_rpn.train_on_batch(X, Y)
P_rpn = model_rpn.predict_on_batch(X)
R = roi_helpers.rpn_to_roi(
P_rpn[0],
P_rpn[1],
C,
use_regr=True,
overlap_thresh=non_max_suppression_overlap_threshold,
max_boxes=non_max_suppression_max_boxes)
# note: calc_iou converts from (x1,y1,x2,y2) to (x,y,w,h) format
X2, Y1, Y2, IouS = roi_helpers.calc_iou(
R, img_data, C, class_mapping)
if X2 is None:
rpn_accuracy_rpn_monitor.append(0)
rpn_accuracy_for_epoch.append(0)
continue
neg_samples = np.where(Y1[0, :, -1] == 1)
pos_samples = np.where(Y1[0, :, -1] == 0)
if len(neg_samples) > 0:
neg_samples = neg_samples[0]
else:
neg_samples = []
if len(pos_samples) > 0:
pos_samples = pos_samples[0]
else:
pos_samples = []
rpn_accuracy_rpn_monitor.append(len(pos_samples))
rpn_accuracy_for_epoch.append((len(pos_samples)))
if C.num_rois > 1:
if len(pos_samples) < C.num_rois // 2:
selected_pos_samples = pos_samples.tolist()
else:
selected_pos_samples = np.random.choice(
pos_samples, C.num_rois // 2,
replace=False).tolist()
try:
selected_neg_samples = np.random.choice(
neg_samples,
C.num_rois - len(selected_pos_samples),
replace=False).tolist()
except:
selected_neg_samples = np.random.choice(
neg_samples,
C.num_rois - len(selected_pos_samples),
replace=True).tolist()
sel_samples = selected_pos_samples + selected_neg_samples
else:
# in the extreme case where num_rois = 1, we pick a random pos or neg sample
if np.random.randint(0, 2):
sel_samples = random.choice(neg_samples)
else:
sel_samples = random.choice(pos_samples)
loss_class = model_classifier.train_on_batch(
[X, X2[:, sel_samples, :]],
[Y1[:, sel_samples, :], Y2[:, sel_samples, :]])
losses[iter_num, 0] = loss_rpn[1]
losses[iter_num, 1] = loss_rpn[2]
losses[iter_num, 2] = loss_class[1]
losses[iter_num, 3] = loss_class[2]
losses[iter_num, 4] = loss_class[3]
progbar.update(
iter_num + 1,
[('rpn_cls', np.mean(losses[:iter_num + 1, 0])),
('rpn_regr', np.mean(losses[:iter_num + 1, 1])),
('detector_cls', np.mean(losses[:iter_num + 1, 2])),
('detector_regr', np.mean(losses[:iter_num + 1, 3]))])
except Exception as e:
print('Exception during training: {}'.format(e))
continue
# Calculate losses after the specified number of iterations
loss_rpn_cls = np.mean(losses[:, 0])
loss_rpn_regr = np.mean(losses[:, 1])
loss_class_cls = np.mean(losses[:, 2])
loss_class_regr = np.mean(losses[:, 3])
class_acc = np.mean(losses[:, 4])
mean_overlapping_bboxes = float(
sum(rpn_accuracy_for_epoch)) / len(rpn_accuracy_for_epoch)
rpn_accuracy_for_epoch = []
if C.verbose:
print('[INFO TRAINING]')
print(
'Mean number of bounding boxes from RPN overlapping ground truth boxes: {}'
.format(mean_overlapping_bboxes))
print('Classifier accuracy for bounding boxes from RPN: {}'.format(
class_acc))
print('Loss RPN classifier: {}'.format(loss_rpn_cls))
print('Loss RPN regression: {}'.format(loss_rpn_regr))
print('Loss Detector classifier: {}'.format(loss_class_cls))
print('Loss Detector regression: {}'.format(loss_class_regr))
print('Elapsed time: {}'.format(time.time() - start_time))
print("Best loss for training: {0:.3f}".format(best_loss_training))
curr_total_loss = loss_rpn_cls + loss_rpn_regr + loss_class_cls + loss_class_regr
val_start_time = time.time()
write_log(tensorboard_callback, train_names, [
loss_rpn_cls, loss_rpn_regr, loss_class_cls, loss_class_regr,
curr_total_loss, class_acc
], epoch_num)
if curr_total_loss < best_loss_training:
model_path = C.model_path[:-5] + "_training.hdf5"
if C.verbose:
print(
'Total training loss decreased from {0:.3f} to {1:.3f}, saving weights to {2}'
.format(best_loss_training, curr_total_loss, model_path))
best_loss_training = curr_total_loss
model_all.save_weights(model_path)
#############
# VALIDATION
#############
if (epoch_num + 1) % validation_interval != 0:
continue
progbar = generic_utils.Progbar(validation_epoch_length)
for iter_num in range(validation_epoch_length):
try:
X, Y, img_data = next(data_gen_val)
loss_rpn = model_rpn.test_on_batch(X, Y)
P_rpn = model_rpn.predict_on_batch(X)
R = roi_helpers.rpn_to_roi(
P_rpn[0],
P_rpn[1],
C,
use_regr=True,
overlap_thresh=non_max_suppression_overlap_threshold,
max_boxes=non_max_suppression_max_boxes)
# note: calc_iou converts from (x1,y1,x2,y2) to (x,y,w,h) format
X2, Y1, Y2, IouS = roi_helpers.calc_iou(
R, img_data, C, class_mapping)
neg_samples = np.where(Y1[0, :, -1] == 1)
pos_samples = np.where(Y1[0, :, -1] == 0)
if len(neg_samples) > 0:
neg_samples = neg_samples[0]
else:
neg_samples = []
if len(pos_samples) > 0:
pos_samples = pos_samples[0]
else:
pos_samples = []
rpn_accuracy_rpn_monitor.append(len(pos_samples))
rpn_accuracy_for_epoch.append((len(pos_samples)))
if C.num_rois > 1:
if len(pos_samples) < C.num_rois // 2:
selected_pos_samples = pos_samples.tolist()
else:
selected_pos_samples = np.random.choice(
pos_samples, C.num_rois // 2,
replace=False).tolist()
try:
selected_neg_samples = np.random.choice(
neg_samples,
C.num_rois - len(selected_pos_samples),
replace=False).tolist()
except:
selected_neg_samples = np.random.choice(
neg_samples,
C.num_rois - len(selected_pos_samples),
replace=True).tolist()
sel_samples = selected_pos_samples + selected_neg_samples
else:
# in the extreme case where num_rois = 1, we pick a random pos or neg sample
selected_pos_samples = pos_samples.tolist()
selected_neg_samples = neg_samples.tolist()
if np.random.randint(0, 2):
sel_samples = random.choice(neg_samples)
else:
sel_samples = random.choice(pos_samples)
loss_class = model_classifier.test_on_batch(
[X, X2[:, sel_samples, :]],
[Y1[:, sel_samples, :], Y2[:, sel_samples, :]])
losses_val[iter_num, 0] = loss_rpn[1]
losses_val[iter_num, 1] = loss_rpn[2]
losses_val[iter_num, 2] = loss_class[1]
losses_val[iter_num, 3] = loss_class[2]
losses_val[iter_num, 4] = loss_class[3]
progbar.update(
iter_num + 1,
[('rpn_cls', np.mean(losses_val[:iter_num + 1, 0])),
('rpn_regr', np.mean(losses_val[:iter_num + 1, 1])),
('detector_cls', np.mean(losses_val[:iter_num + 1, 2])),
('detector_regr', np.mean(losses_val[:iter_num + 1, 3]))])
except Exception as e:
#print('Exception during validation: {}'.format(e))
continue
# Computer aggregated losses
loss_rpn_cls = np.mean(losses_val[:, 0])
loss_rpn_regr = np.mean(losses_val[:, 1])
loss_class_cls = np.mean(losses_val[:, 2])
loss_class_regr = np.mean(losses_val[:, 3])
class_acc = np.mean(losses[:, 4])
mean_overlapping_bboxes = float(
sum(rpn_accuracy_for_epoch)) / len(rpn_accuracy_for_epoch)
rpn_accuracy_for_epoch = []
curr_total_loss = loss_rpn_cls + loss_rpn_regr + loss_class_cls + loss_class_regr
write_log(tensorboard_callback, val_names, [
loss_rpn_cls, loss_rpn_regr, loss_class_cls, loss_class_regr,
curr_total_loss, class_acc
], epoch_num)
if C.verbose:
print('[INFO VALIDATION]')
print(
'Mean number of bounding boxes from RPN overlapping ground truth boxes: {}'
.format(mean_overlapping_bboxes))
print('Classifier accuracy for bounding boxes from RPN: {}'.format(
class_acc))
print('Loss RPN classifier: {}'.format(loss_rpn_cls))
print('Loss RPN regression: {}'.format(loss_rpn_regr))
print('Loss Detector classifier: {}'.format(loss_class_cls))
print('Loss Detector regression: {}'.format(loss_class_regr))
print(
"Current validation loss: {0:.3f}, Best validation loss: {1:.3f} at epoch: {2}"
.format(curr_total_loss, best_total_loss_validation,
best_loss_epoch))
print('Elapsed time: {}'.format(time.time() - val_start_time))
if curr_total_loss < best_total_loss_validation:
if C.verbose:
print(
'Total validation loss decreased from {0:.3f} to {1:.3f}, saving weights to {2}'
.format(best_total_loss_validation, curr_total_loss,
C.model_path))
best_total_loss_validation = curr_total_loss
best_loss_rpn_cls = loss_rpn_cls
best_loss_rpn_regr = loss_rpn_regr
best_loss_class_cls = loss_class_cls
best_loss_class_regr = loss_class_regr
best_class_acc = class_acc
best_loss_epoch = epoch_num
model_all.save_weights(C.model_path)
epochs_without_improvement = 0
else:
epochs_without_improvement += validation_interval
if epochs_without_improvement > early_stopping:
print(
"Early stopping training after {0} epochs without improvement on validation set"
.format(epochs_without_improvement))
break
if epochs_without_improvement > learning_rate_reduction_patience:
current_learning_rate = K.get_value(model_classifier.optimizer.lr)
new_learning_rate = current_learning_rate * learning_rate_reduction_factor
print(
"Not improved validation accuracy for {0} epochs. Reducing learning rate from {1} to {2}"
.format(learning_rate_reduction_patience,
current_learning_rate, new_learning_rate))
K.set_value(model_classifier.optimizer.lr, new_learning_rate)
K.set_value(model_rpn.optimizer.lr, new_learning_rate)
K.set_value(model_all.optimizer.lr, new_learning_rate)
end_time = time.time()
execution_time_in_seconds = round(end_time - start_time)
print("Execution time: {0:.1f}s".format(end_time - start_time))
notification_message = "Training on {0} dataset with model {1} and configuration {2} finished. " \
"Val. accuracy: {3:0.5f}%".format("muscima_pp",
model_name,
configuration_name,
best_class_acc * 100)
TelegramNotifier.send_message_via_telegram(notification_message)
today = "{0:02d}.{1:02d}.{2}".format(start_of_training.day,
start_of_training.month,
start_of_training.year)
total_number_of_images = len(training_images) + len(
validation_images) + len(test_images)
GoogleSpreadsheetReporter.append_result_to_spreadsheet(
dataset_size=total_number_of_images,
model_name=model_name,
configuration_name=configuration_name,
data_augmentation="",
early_stopping=early_stopping,
reduction_patience=learning_rate_reduction_patience,
learning_rate_reduction_factor=learning_rate_reduction_factor,
optimizer="Adadelta",
initial_learning_rate=1.0,
non_max_suppression_overlap_threshold=
non_max_suppression_overlap_threshold,
non_max_suppression_max_boxes=non_max_suppression_max_boxes,
validation_accuracy=best_class_acc,
validation_total_loss=best_total_loss_validation,
best_loss_rpn_cls=best_loss_rpn_cls,
best_loss_rpn_regr=best_loss_rpn_regr,
best_loss_class_cls=best_loss_class_cls,
best_loss_class_regr=best_loss_class_regr,
date=today,
datasets="muscima_pp",
execution_time_in_seconds=execution_time_in_seconds)
def train_fasterrcnn():
# config for data argument
cfg = config.Config()
cfg.balanced_classes = False
cfg.use_horizontal_flips = True
cfg.use_vertical_flips = True
cfg.rot_90 = True
cfg.num_rois = 100 #50# 对于星图杯的光学遥感飞机检测，应该改为50+
cfg.anchor_box_scales = [16,32,64,128,256]#[41,70,120,20,90]
cfg.anchor_box_ratios = [[1,0.7],[1,1.4],[1,1],[1.4,1],[0.7,1]]#[[1,1.4],[1,0.84],[1,1.17],[1,0.64],[1,1]]
cfg.rpn_stride = 8
cfg.im_size = 512
cfg.num_epochs = 100
cfg.epoch_length = 150#1462
cfg.base_net_weights = os.path.join('./model/', nn.get_weight_path())
# TODO: the only file should to be change for other data to train
cfg.model_path = './model/kitti_frcnn_last.hdf5'
cfg.simple_label_file = 'DOTA2018_OpticalAircraft_bboxes.txt'#'kitti_simple_label.txt'#'E:/Xingtubei/official_datas/OpticalAircraft/laptop_Chreoc_OpticalAircraft_bboxes.txt' # '/media/liuhuaqing/Elements/Xingtubei/official_datas/OpticalAircraft/Chreoc_OpticalAircraft_bboxes.txt'#'F:/Xingtubei/official_datas/OpticalAircraft/Chreoc_OpticalAircraft_bboxes.txt' # 'kitti_simple_label.txt'
all_images, classes_count, class_mapping = get_data(cfg.simple_label_file)#读取数据集，cv2.imread()要求数据里不能有中文路径
if 'bg' not in classes_count:#'bg'应该是代表背景
classes_count['bg'] = 0 # =0表示训练数据中没有“背景”这一类别
class_mapping['bg'] = len(class_mapping)
cfg.class_mapping = class_mapping
with open(cfg.config_save_file, 'wb') as config_f:
pickle.dump(cfg, config_f)
print('Config has been written to {}, and can be loaded when testing to ensure correct results'.format(
cfg.config_save_file))
inv_map = {v: k for k, v in class_mapping.items()}#class_mapping的逆向map
print('Training images per class:')
pprint.pprint(classes_count)
print('Num classes (including bg) = {}'.format(len(classes_count)))
random.shuffle(all_images)
num_imgs = len(all_images)
train_imgs = [s for s in all_images if s['imageset'] == 'trainval'] #训练集，列表形式，列表中的元素是字典
val_imgs = [s for s in all_images if s['imageset'] == 'test'] #验证集，列表形式，列表中的元素是字典
print('Num train samples {}'.format(len(train_imgs)))
print('Num val samples {}'.format(len(val_imgs)))
data_gen_train = data_generators.get_anchor_gt(train_imgs, classes_count, cfg, nn.get_img_output_length,
K.image_dim_ordering(), mode='train')#数据扩增，然后生成frcnn所需的训练数据（如：图片、rpn的梯度等等）
data_gen_val = data_generators.get_anchor_gt(val_imgs, classes_count, cfg, nn.get_img_output_length,
K.image_dim_ordering(), mode='val')#数据扩增，然后生成frcnn所需的验证数据（如：图片、rpn的梯度等等）
# 根据keras实际用的后端，定义相应的输入数据维度，因为两类后端的维度顺序不一样
if K.image_dim_ordering() == 'th':
input_shape_img = (3, None, None)#当后端是thaneo
else:
input_shape_img = (None,None,3)#(None, None, 3)#当后端是tensorflow
img_input = Input(shape=input_shape_img) # 输入图片
roi_input = Input(shape=(None, 4)) # 输入人工标注的roi坐标，4表示x1,y1,x2,y2
# define the base network (resnet here, can be VGG, Inception, etc)
shared_layer,shared_layers_stage3,shared_layers_stage4 = nn.nn_base(img_input, trainable=True)# shared_layers是frcnn网络底部那些共享的层,在这里是ResNet。由nn定义好
# define the RPN, built on the base layers
num_anchors = len(cfg.anchor_box_scales) * len(cfg.anchor_box_ratios)
rpn_stage3 = nn.rpn(shared_layers_stage3, num_anchors)
print(rpn_stage3[1].shape)
rpn_stage4 = nn.rpn(shared_layers_stage4, num_anchors) # [x_class, x_regr, base_layers]
print(rpn_stage4[1].shape)
# x_class的shape是(?,sharelayer的w/2,sharelayer的h/2,scale数*ratio数),x_regr的shape是(?,sharelayer的w/2,sharelayer的h/2,4*scale数*ratio数)
rpn = nn.rpn(shared_layer,num_anchors)
print(rpn[1].shape)
# 在这里合并两个rpn分支
classifier = nn.classifier(shared_layer, roi_input, cfg.num_rois, nb_classes=len(classes_count), trainable=True)
model_rpn = Model(inputs=img_input, outputs=rpn[:2])#rpn网络由keras_frcnn/resnet定义。rpn[:2]的前两个元素分别表示rpn网络的分类输出和回归输出
model_classifier = Model(inputs=[img_input, roi_input], outputs=classifier)#Keras的函数式模型为Model，即广义的拥有输入和输出的模型
# this is a model that holds both the RPN and the classifier, used to load/save weights for the models
model_all = Model(inputs=[img_input, roi_input], outputs=rpn[:2] + classifier)#rpn[:2]+classifier的含义是？？？？？？
try:
# 尝试载入已训练网络权值
print('loading weights from {}'.format(cfg.base_net_weights))
model_rpn.load_weights(cfg.model_path, by_name=True)
model_classifier.load_weights(cfg.model_path, by_name=True)
except Exception as e:
print(e)
print('Could not load pretrained model weights. Weights can be found in the keras application folder '
'https://github.com/fchollet/keras/tree/master/keras/applications')
optimizer = Adam(lr=1e-5)# 定义一个Adam求解器，学习率lr
optimizer_classifier = Adam(lr=1e-5)# 定义一个Adam求解器，学习率lr
# num_anchors等于9
model_rpn.compile(optimizer=optimizer,
loss=[losses_fn.rpn_loss_cls(num_anchors), losses_fn.rpn_loss_regr(num_anchors)])
model_classifier.compile(optimizer=optimizer_classifier,
loss=[losses_fn.class_loss_cls, losses_fn.class_loss_regr(len(classes_count) - 1)],
metrics={'dense_class_{}'.format(len(classes_count)): 'accuracy'})
model_all.compile(optimizer='sgd', loss='mae') #mae表示绝对值均差
epoch_length = cfg.epoch_length # epoch_length是一个周期的迭代次数（也等于训练数据量）。每迭代epoch_length次就检查一次是否要保存网络权值，然后重置iter_num = 0
num_epochs = int(cfg.num_epochs)
iter_num = 0 # 迭代次数的初值
losses = np.zeros((epoch_length, 5)) # 初始化loss数组，记录每个周期的loss
rpn_accuracy_rpn_monitor = [] # 初始化一个数组，记录rpn的训练过程中的精度变化
rpn_accuracy_for_epoch = [] # 初始化一个数组，记录rpn的每个训练周期的的精度变化
start_time = time.time() # 开始训练的时间
best_loss = 5.0 #2.1 #np.Inf # 训练以来最小的loss
class_mapping_inv = {v: k for k, v in class_mapping.items()} # class_mapping_inv是一个字典，key是目标类别编号，value是类别名称
print('Starting training')
vis = True
for epoch_num in range(num_epochs):
progbar = generic_utils.Progbar(epoch_length) # 生成一个进度条对象
print('Epoch {}/{}'.format(epoch_num + 1, num_epochs)) # 输出当前训练周期数/总周期数
while True:# 什么时候才结束这个循环？答：第247行的break(每迭代epoch_length次)
try:
if len(rpn_accuracy_rpn_monitor) == epoch_length and cfg.verbose: # 每epoch_length次训练周期就在窗口显示一次RPN平均精度
mean_overlapping_bboxes = float(sum(rpn_accuracy_rpn_monitor)) / len(rpn_accuracy_rpn_monitor)
rpn_accuracy_rpn_monitor = []
print(
'Average number of overlapping bounding boxes from RPN = {} for {} previous iterations'.format(
mean_overlapping_bboxes, epoch_length))
if mean_overlapping_bboxes == 0:
print('RPN is not producing bounding boxes that overlap'
' the ground truth boxes. Check RPN settings or keep training.')
# X是原图，如kitti尺寸是(1,600,1987,3)。
# Y是label，是有两个元素的list，
# 其中第一个元素是类别，具体：shape是(1,share_layer_h,share_layer_w,2*scale数*ratio数),前一个元素为1（0）则表示是（不是）正或负样本，后一个为1（0）则表示是（不是）正样本
# 第二个元素是bbox，具体：shape是(1,share_layer_h,share_layer_w,8*scale数*ratio数),前四个元素表示是不是正样，后四个元素才是bbox#为什么来个repeat赋值给前面一半
# img_data是字典，包含文件名、尺寸、人工标记的roi和类别等
#
X, Y, img_data = next(data_gen_train)
loss_rpn = model_rpn.train_on_batch(X, Y)#为什么Y的尺寸与P_rpn的尺寸不同？为什么loss_rpn的尺寸是3，含义是什么，在哪里定义的？
P_rpn = model_rpn.predict_on_batch(X)#P_rpn的尺寸是(1, 124, 38, 9) (1, 124, 38, 36)
result = roi_helpers.rpn_to_roi(P_rpn[0], P_rpn[1], cfg, K.image_dim_ordering(), use_regr=True,
overlap_thresh=0.7,
max_boxes=300) #result的尺寸是300*4
# note: calc_iou converts from (x1,y1,x2,y2) to (x,y,w,h) format
# X2的尺寸是100*4，Y1的尺寸是1*100*8（8=训练集中目标类别总数），IouS尺寸是100
X2, Y1, Y2, IouS = roi_helpers.calc_iou(result, img_data, cfg, class_mapping)#Y2的尺寸是1*1*56,56=28*2，（28=4*7）前28是coords，后28是labels(是该类别则标1)
if X2 is None:
rpn_accuracy_rpn_monitor.append(0)
rpn_accuracy_for_epoch.append(0)
continue
neg_samples = np.where(Y1[0, :, -1] == 1)#Y1的尺寸是1*1*8表示分类预测结果，最后一个元素为1表示是背景
pos_samples = np.where(Y1[0, :, -1] == 0)
if len(neg_samples) > 0:
neg_samples = neg_samples[0]
else:
neg_samples = []
if len(pos_samples) > 0:
pos_samples = pos_samples[0]
else:
pos_samples = []
rpn_accuracy_rpn_monitor.append(len(pos_samples))
rpn_accuracy_for_epoch.append((len(pos_samples)))
if cfg.num_rois > 1:
if len(pos_samples) < cfg.num_rois // 2:
selected_pos_samples = pos_samples.tolist()
else:
selected_pos_samples = np.random.choice(pos_samples, cfg.num_rois // 2, replace=False).tolist()
try:
selected_neg_samples = np.random.choice(neg_samples, cfg.num_rois - len(selected_pos_samples),
replace=False).tolist()
except:
selected_neg_samples = np.random.choice(neg_samples, cfg.num_rois - len(selected_pos_samples),
replace=True).tolist()
sel_samples = selected_pos_samples + selected_neg_samples
else:
# in the extreme case where num_rois = 1, we pick a random pos or neg sample
selected_pos_samples = pos_samples.tolist()
selected_neg_samples = neg_samples.tolist()
if np.random.randint(0, 2):
sel_samples = random.choice(neg_samples)
else:
sel_samples = random.choice(pos_samples)
loss_class = model_classifier.train_on_batch([X, X2[:, sel_samples, :]],
[Y1[:, sel_samples, :], Y2[:, sel_samples, :]])#用rpn输出的roi输入给classifier
losses[iter_num, 0] = loss_rpn[1]
losses[iter_num, 1] = loss_rpn[2]
losses[iter_num, 2] = loss_class[1]
losses[iter_num, 3] = loss_class[2]
losses[iter_num, 4] = loss_class[3]
iter_num += 1
progbar.update(iter_num,
[('rpn_cls', np.mean(losses[:iter_num, 0])), ('rpn_regr', np.mean(losses[:iter_num, 1])),
('detector_cls', np.mean(losses[:iter_num, 2])),('detector_regr', np.mean(losses[:iter_num, 3]))])
if iter_num == epoch_length:# 每迭代epoch_length次就检查一次是否要保存网络权值，然后重置iter_num = 0
loss_rpn_cls = np.mean(losses[:, 0])
loss_rpn_regr = np.mean(losses[:, 1])
loss_class_cls = np.mean(losses[:, 2])
loss_class_regr = np.mean(losses[:, 3])
class_acc = np.mean(losses[:, 4])
mean_overlapping_bboxes = float(sum(rpn_accuracy_for_epoch)) / len(rpn_accuracy_for_epoch)
rpn_accuracy_for_epoch = []
if cfg.verbose:
print('Mean number of bounding boxes from RPN overlapping ground truth boxes: {}'.format(
mean_overlapping_bboxes))
print('Classifier accuracy for bounding boxes from RPN: {}'.format(class_acc))
print('Loss RPN classifier: {}'.format(loss_rpn_cls))
print('Loss RPN regression: {}'.format(loss_rpn_regr))
print('Loss Detector classifier: {}'.format(loss_class_cls))
print('Loss Detector regression: {}'.format(loss_class_regr))
print('Elapsed time: {}'.format(time.time() - start_time))
curr_loss = loss_rpn_cls + loss_rpn_regr + loss_class_cls + loss_class_regr
iter_num = 0
start_time = time.time()
if curr_loss < best_loss:
if cfg.verbose:
print('Total loss decreased from {} to {}, saving weights'.format(best_loss, curr_loss))
best_loss = curr_loss
model_all.save_weights(cfg.model_path)
break
except Exception as e:
print('Exception: {}'.format(e))
# save model
model_all.save_weights(cfg.model_path)
continue
print('Training complete, exiting.')
def validate():
print('Begining Validation: ')
while True:
X_val, Xtherm_val, Y_val, img_data_val = next(data_gen_val)
loss_rpn_val = model_rpn.evaluate([X_val, Xtherm_val], Y_val, batch_size = 256, verbose = 0)
P_rpn_val = model_rpn.predict_on_batch([X_val, Xtherm_val])
R = roi_helpers.rpn_to_roi(P_rpn_val[0], P_rpn_val[1], C, K.image_dim_ordering(), use_regr=True, overlap_thresh=0.7, max_boxes=300)
# note: calc_iou converts from (x1,y1,x2,y2) to (x,y,w,h) format
X2, Y1, Y2, IouS = roi_helpers.calc_iou(R, img_data_val, C, class_mapping)
if X2 is None:
rpn_accuracy_rpn_monitor.append(0)
rpn_accuracy_for_epoch.append(0)
continue
neg_samples = np.where(Y1[0, :, -1] == 1)
pos_samples = np.where(Y1[0, :, -1] == 0)
if len(neg_samples) > 0:
neg_samples = neg_samples[0]
else:
neg_samples = []
if len(pos_samples) > 0:
pos_samples = pos_samples[0]
else:
pos_samples = []
rpn_accuracy_rpn_monitor.append(len(pos_samples))
rpn_accuracy_for_epoch.append((len(pos_samples)))
if C.num_rois > 1:
if len(pos_samples) < C.num_rois//2:
selected_pos_samples = pos_samples.tolist()
else:
selected_pos_samples = np.random.choice(pos_samples, C.num_rois//2, replace=False).tolist()
try:
selected_neg_samples = np.random.choice(neg_samples, C.num_rois - len(selected_pos_samples), replace=False).tolist()
except:
selected_neg_samples = np.random.choice(neg_samples, C.num_rois - len(selected_pos_samples), replace=True).tolist()
sel_samples = selected_pos_samples + selected_neg_samples
else:
# in the extreme case where num_rois = 1, we pick a random pos or neg sample
selected_pos_samples = pos_samples.tolist()
selected_neg_samples = neg_samples.tolist()
if np.random.randint(0, 2):
sel_samples = random.choice(neg_samples)
else:
sel_samples = random.choice(pos_samples)
loss_class_val = model_classifier.evaluate([Xrgb, Xtherm], X2[:, sel_samples, :]], [Y1[:, sel_samples, :], Y2[:, sel_samples, :]])
val_losses[val_iter_num, 0] = loss_rpn_val[1]
val_losses[val_iter_num, 1] = loss_rpn_val[2]
val_losses[val_iter_num, 2] = loss_class_val[1]
val_losses[val_iter_num, 3] = loss_class_val[2]
val_losses[val_iter_num, 4] = loss_class_val[3]
val_iter_num += 1
print(str(val_iter_num)+'/'+str(epoch_length) +' val_rpn_cls: '+str(np.mean(val_losses[:val_iter_num, 0])) +' val_rpn_regr: '+str(np.mean(val_losses[:val_iter_num, 1]))+
' val_detector_cls: '+str(np.mean(val_losses[:val_iter_num, 2]))+' val_detector_regr: '+str(np.mean(val_losses[:val_iter_num, 3])))
if val_iter_num == epoch_length-1:
loss_rpn_cls = np.mean(losses[:, 0])
loss_rpn_regr = np.mean(losses[:, 1])
loss_class_cls = np.mean(losses[:, 2])
loss_class_regr = np.mean(losses[:, 3])
class_acc = np.mean(losses[:, 4])
mean_overlapping_bboxes = float(sum(rpn_accuracy_for_epoch)) / len(rpn_accuracy_for_epoch)
rpn_accuracy_for_epoch = []
if C.verbose:
print('Mean number of bounding boxes from RPN overlapping ground truth boxes: {}'.format(mean_overlapping_bboxes))
print('Classifier accuracy for bounding boxes from RPN: {}'.format(class_acc))
print('Loss RPN classifier: {}'.format(loss_rpn_cls))
print('Loss RPN regression: {}'.format(loss_rpn_regr))
print('Loss Detector classifier: {}'.format(loss_class_cls))
print('Loss Detector regression: {}'.format(loss_class_regr))
print('Elapsed time: {}'.format(time.time() - start_time))
curr_loss = loss_rpn_cls + loss_rpn_regr + loss_class_cls + loss_class_regr
iter_num = 0
val_iter_num = 0
start_time = time.time()
if curr_loss < best_loss:
if C.verbose:
print('Total loss decreased from {} to {}, saving weights'.format(best_loss,curr_loss))
best_loss = curr_loss
model_all.save_weights('New ResNet50 Model')
break
################
best_loss = np.Inf
class_mapping_inv = {v: k for k, v in class_mapping.items()}
print('Starting training')
vis = True
for epoch_num in range(num_epochs):
progbar = generic_utils.Progbar(epoch_length)
print('Epoch {}/{}'.format(epoch_num + 1, num_epochs))
while True:
try:
img_data, seq_numpy, x_img = next(data_gen_train)
X2, Y1 = roi_helpers.calc_iou(img_data, C, class_mapping)
loss_class = model_classifier.train_on_batch(
[X2[:, :, :], seq_numpy], [Y1[:, :, :]])
losses[iter_num, 0] = loss_class
iter_num += 1
write_log(tensorboard, ['loss_class'], [loss_class],
iter_num_tensorboard)
iter_num_tensorboard += 1
progbar.update(iter_num,
[('class_loss', np.mean(losses[:iter_num, 0]))])
if iter_num == epoch_length:
loss_class_cls = np.mean(losses[:, 0])
curr_loss = loss_class_cls
write_log(tensorboard, ['total train loss'], [curr_loss],
def Train_frcnn(train_path = './data/FlickrLogos_47/train/', # path to the text file containing the data
class_name = './data/FlickrLogos_47/className2ClassID.txt',
network_arch = 'vgg', # the type of the base faster rcnn network architecture
num_epochs = 50, # num of epochs
output_weight_path = './models/model_frcnn_47.hdf5', # path to save the model_all.weights as hdf5
preprocessing_function = None,
config_filename="config_47.pickle",
input_weights_path = './models/vgg16_weights_tf_dim_ordering_tf_kernels.h5',
train_rpn = True,
train_final_classifier = True,
train_base_nn = True,
losses_to_watch = ['rpn_cls','rpn_reg','final_cls','final_reg'],
tb_log_dir="log",
num_rois=32,
horizontal_flips=False,
vertical_flips=False,
rot_90=False,
anchor_box_scales=[128, 256, 512],
anchor_box_ratios=[[1, 1], [1./math.sqrt(2), 2./math.sqrt(2)], [2./math.sqrt(2), 1./math.sqrt(2)]],
im_size=600,
rpn_stride=16, # depends on network architecture
visualize_model = None,
verify_trainable = True,
optimizer_rpn = Adam(lr=1e-5),
optimizer_classifier = Adam(lr=1e-5),
validation_interval = 3,
rpn_min_overlap = 0.3,
rpn_max_overlap = 0.7,
classifier_min_overlap = 0.1,
classifier_max_overlap = 0.5,
rpn_nms_threshold = 0.7, # original implementation
seed=5000
):
"""
Trains a Faster RCNN for object detection in keras
NOTE: This trains 2 models namely model_rpn and model_classifer with the same shared base_nn (fixed feature extractor)
Keyword Arguments
train_path -- str: path to the text file or pascal_voc (no Default)
network_arch --object: the full faster rcnn network .py file passed as an object (no default)
num_epochs -- int: number of epochs to train (no Default)
output_weight_path --str: path to save the frcnn weights (no Default)
preprocessing_function --function: Optional preprocessing function (must be defined like given in keras docs) (Default None)
config_filename --str: Path to save the config file. Used when testing (Default "config.pickle")
input_weight_path --str: Path to hdf5 file containing weights for the model (Default None)
you can pass path to both classification and detection checkpoints as long as the names dont' change
train_rpn --bool: whether to train the rpn layer (Default True)
train_final_classifier --bool:Whether to train the final_classifier (Fast Rcnn layer) (Default True)
train_base_nn --bool:Whether to train the base_nn/fixed_feature_extractor (Default True)
losses_to_watch --list: A list of losses to watch (Default ['rpn_cls','rpn_reg','final_cls','final_reg']).
The losses in this list are added and then weights are saved wrt to that.
The list can contain any combination of the above 4 only.
tb_log_dir --str: path to log dir for tensorboard logging (Default 'log')
num_rois --int: The number of rois to use at once (Default = 32)
horizontal_flips --bool: augment training data by horizontal flips (Default False)
vertical_flips --bool: augment training data by vertical flips (Default False)
rot_90 --bool: augment training data by 90 deg rotations (Default False)
anchor_box_scales --list: The list of anchor box scales to use (Default [128,256,512])
anchor_box ratios --list of list: The list of anchorbox aspect ratios to use (Default [[1, 1], [1./math.sqrt(2), 2./math.sqrt(2)], [2./math.sqrt(2), 1./math.sqrt(2)]])
im_size --int: The size to resize the image (Default 600). This is the smallest side of Pascal VOC format
rpn_stride --int: The stride for rpn (Default = 16)
visualize_model --str: Path to save the model as .png file
verify_trainable --bool: print layer wise names and prints if it is trainable or not (Default True)
optimizer_rpn --keras.optimizer: The optimizer for rpn (Default Adam(lr=1e-5))
optimizer_classifier --keras.optimizer: The optimizer for classifier (Default Adam(lr=1e-5))
validation_interval --int: The frequency (in epochs) to do validation. supply 0 if no validation
rpn_min_overlap --float: (0,1) The Min IOU in rpn layer (Default 0.3) (original implementation)
rpn_max_overlap --float: (0,1) The max IOU in rpn layer (Default 0.7) (original implementation)
classifier_min_overlap --float: (0,1) same as above but in final classifier (Default 0.1) (original implementation)
classifier_max_overlap --float: (0,1) same as above (Default 0.5) (original implementation)
rpn_nms_threshold --float :(0,1) The threshold above which to supress the bbox using Non max supression in rpn (Default 0.7)(from original implementation)
seed --int: To seed the random shuffling of training data (Default = 5000)
Performing alternating training:
- Use the train_rpn,train_final_classifier and train_base_nn boolean arguments to accomplish
alternating training.
- While using the above arguments change the members of losses_to_watch = ['rpn_cls','rpn_reg','final_cls','final_reg']
accordingly else it will throw error
- for eg if you are training only the base_nn and the rpn set:
train_rpn = True
train_base_nn = True
train_final_classifier = False
losses_to_watch = ['rpn_cls','rpn_reg'] (do not include 'final_cls', 'final_reg')
OUTPUT:
prints the training log. Does not return anything
Save details:
1.saves the weights of the full FRCNN model as .h5
2.saves a tensorboard file
3.saves the history of weights saved in ./saving_log.txt so that it can be known at which epoch the model is saved
4.saves the model configuration as a .pickle file
5.optionally saves the full FRCNN architecture as .png
NOTE:
as of now the batch size = 1
Prints loss = 0 for losses from model which is not being trained
TODO: The training is a bit slow because of the data generation step. Generate_data in multiple threads and queue them for faster training
"""
check_list = ['rpn_cls','rpn_reg','final_cls','final_reg']
for n in losses_to_watch:
if n not in check_list:
raise ValueError("unsupported loss the supported losses are: {}".format(check_list))
if not train_rpn:
if "rpn_cls" in losses_to_watch or "rpn_reg" in losses_to_watch:
raise ValueError("Cannot watch rpn_cls and rpn_reg when train_rpn == False")
if not train_final_classifier:
if "final_cls" in losses_to_watch or "final_reg" in losses_to_watch:
raise ValueError("cannot watch final_cls and final_reg when train_final_classifier == False")
if network_arch == 'vgg':
from keras_frcnn import nn_arch_vgg16 as nn
elif network_arch == 'resnet50':
from keras_frcnn import nn_arch_resnet50 as nn
else:
print('Not a valid model')
raise ValueError
random.seed(seed)
np.random.seed(seed)
# pass the settings from the function call, and persist them in the config object
C = config.Config()
C.rpn_max_overlap = rpn_max_overlap
C.rpn_min_overlap = rpn_min_overlap
C.classifier_min_overlap = classifier_min_overlap
C.classifier_max_overlap = classifier_max_overlap
C.anchor_box_scales = anchor_box_scales
C.anchor_box_ratios = anchor_box_ratios
C.im_size = im_size
C.use_horizontal_flips = bool(horizontal_flips)
C.use_vertical_flips = bool(vertical_flips)
C.rot_90 = bool(rot_90)
C.rpn_stride=rpn_stride
C.rpn_nms_threshold = rpn_nms_threshold
C.weights_all_path = output_weight_path
C.num_rois = int(num_rois)
# check if weight path was passed via command line
if input_weights_path:
C.initial_weights = input_weights_path
all_imgs, classes_count, class_mapping = get_data(train_path, class_name)
print("The class mapping is:")
print(class_mapping)
if 'bg' not in classes_count:
classes_count['bg'] = 0
class_mapping['bg'] = len(class_mapping)
C.class_mapping = class_mapping
print('Training images per class:')
pprint.pprint(classes_count)
print('Num classes (including bg) = {}'.format(len(classes_count)))
with open(config_filename, 'wb') as config_f:
pickle.dump(C,config_f)
print('Config has been written to {}, and can be loaded when testing to ensure correct results'.format(config_filename))
np.random.shuffle(all_imgs)
train_imgs = [s for s in all_imgs if s['imageset'] == 'train']
val_imgs = [s for s in all_imgs if s['imageset'] == 'test']
print('Num train samples {}'.format(len(train_imgs)))
print('Num val samples {}'.format(len(val_imgs)))
input_shape_img = (None, None, 3)
img_input = Input(shape=input_shape_img)
roi_input = Input(shape=(None, 4))
# define the base network (resnet here, can be VGG, Inception, etc)
shared_layers = nn.nn_base(img_input,trainable = train_base_nn)
# define the RPN, built on the base layers
num_anchors = len(C.anchor_box_scales) * len(C.anchor_box_ratios)
rpn = nn.rpn(shared_layers, num_anchors,trainable = train_rpn)
# define the classifier, built on base layers
classifier = nn.classifier(shared_layers, roi_input, C.num_rois, len(classes_count),trainable = train_final_classifier)
# create models
model_base = Model(img_input,shared_layers) # for computing the output shape
model_rpn = Model(img_input, rpn[:2]) # used for training
model_classifier = Model([img_input, roi_input], classifier) # used for training
# this is a model that holds both the RPN and the classifier, used to load/save and freeze/unfreeze weights for the models
model_all = Model([img_input, roi_input], rpn[:2] + classifier)
# tensorboard
tbCallBack = TensorBoard(log_dir=tb_log_dir, histogram_freq=1,write_graph=False, write_images=False)
tbCallBack.set_model(model_all)
#NOTE: both model_rpn and model_classifer contains the base_nn
try:
print('loading weights from {}'.format(C.initial_weights))
model_all.load_weights(C.initial_weights, by_name=True)
except:
print('Could not load pretrained model weights')
# number of trainable parameters
trainable_count = int(np.sum([K.count_params(p) for p in set(model_all.trainable_weights)]))
non_trainable_count = int(np.sum([K.count_params(p) for p in set(model_all.non_trainable_weights)]))
print('Total params: {:,}'.format(trainable_count + non_trainable_count))
print('Trainable params: {:,}'.format(trainable_count))
print('Non-trainable params: {:,}'.format(non_trainable_count))
if verify_trainable:
for layer in model_all.layers:
print(layer.name,layer.trainable)
model_rpn.compile(optimizer=optimizer_rpn, loss=[Losses.rpn_loss_cls(num_anchors), Losses.rpn_loss_regr(num_anchors)])
model_classifier.compile(optimizer=optimizer_classifier, loss=[Losses.class_loss_cls, Losses.class_loss_regr(len(classes_count)-1)], metrics={'dense_class_{}'.format(len(classes_count)): 'accuracy'})
model_all.compile(optimizer='sgd', loss='mse')
# save model_all as png for visualization
if visualize_model != None:
# from IPython.display import SVG
# from keras.utils.vis_utils import model_to_dot
# SVG(model_to_dot(model_all).create(prog='dot', format='svg'))
plot_model(model=model_all,to_file=visualize_model,show_shapes=True,show_layer_names=True)
epoch_length = len(train_imgs)
validation_epoch_length=len(val_imgs)
num_epochs = int(num_epochs)
iter_num = 0
# train and valid data generator
data_gen_train = data_generators.get_anchor_gt(train_imgs, classes_count, C, model_base, K.image_dim_ordering(), preprocessing_function ,mode='train')
data_gen_val = data_generators.get_anchor_gt(val_imgs, classes_count, C, model_base,K.image_dim_ordering(), preprocessing_function ,mode='val')
losses_val=np.zeros((validation_epoch_length,5))
losses = np.zeros((epoch_length, 5))
rpn_accuracy_rpn_monitor = []
rpn_accuracy_for_epoch = []
start_time = time.time()
best_loss = np.Inf
val_best_loss = np.Inf
val_best_loss_epoch = 0
print('Starting training')
def write_log(callback, names, logs, batch_no):
for name, value in zip(names, logs):
summary = tf.Summary()
summary_value = summary.value.add()
summary_value.simple_value = value
summary_value.tag = name
callback.writer.add_summary(summary, batch_no)
callback.writer.flush()
train_names = ['train_loss_rpn_cls', 'train_loss_rpn_reg','train_loss_class_cls','train_loss_class_reg','train_total_loss','train_acc']
val_names = ['val_loss_rpn_cls', 'val_loss_rpn_reg','val_loss_class_cls','val_loss_class_reg','val_total_loss','val_acc']
for epoch_num in range(num_epochs):
progbar = generic_utils.Progbar(epoch_length)
print('Epoch {}/{}'.format(epoch_num + 1, num_epochs))
while True:
try:
if len(rpn_accuracy_rpn_monitor) == epoch_length and C.verbose:
mean_overlapping_bboxes = float(sum(rpn_accuracy_rpn_monitor))/len(rpn_accuracy_rpn_monitor)
rpn_accuracy_rpn_monitor = []
print('Average number of overlapping bounding boxes from RPN = {} for {} previous iterations'.format(mean_overlapping_bboxes, epoch_length))
if mean_overlapping_bboxes == 0:
print('RPN is not producing bounding boxes that overlap the ground truth boxes. Check RPN settings or keep training.')
X, Y, img_data = next(data_gen_train)
if train_rpn:
loss_rpn = model_rpn.train_on_batch(X, Y)
P_rpn = model_rpn.predict_on_batch(X)
R = roi_helpers.rpn_to_roi(P_rpn[0], P_rpn[1], C, K.image_dim_ordering(), use_regr=True, overlap_thresh=C.rpn_nms_threshold,flag="train")
# note: calc_iou converts from (x1,y1,x2,y2) to (x,y,w,h) format
X2, Y1, Y2, IouS = roi_helpers.calc_iou(R, img_data, C, class_mapping)
if X2 is None:
rpn_accuracy_rpn_monitor.append(0)
rpn_accuracy_for_epoch.append(0)
continue
neg_samples = np.where(Y1[0, :, -1] == 1)
pos_samples = np.where(Y1[0, :, -1] == 0)
if len(neg_samples) > 0:
neg_samples = neg_samples[0]
else:
neg_samples = []
if len(pos_samples) > 0:
pos_samples = pos_samples[0]
else:
pos_samples = []
rpn_accuracy_rpn_monitor.append(len(pos_samples))
rpn_accuracy_for_epoch.append((len(pos_samples)))
if C.num_rois > 1:
if len(pos_samples) < C.num_rois//2:
selected_pos_samples = pos_samples.tolist()
else:
selected_pos_samples = np.random.choice(pos_samples, C.num_rois//2, replace=False).tolist()
try:
selected_neg_samples = np.random.choice(neg_samples, C.num_rois - len(selected_pos_samples), replace=False).tolist()
except:
selected_neg_samples = np.random.choice(neg_samples, C.num_rois - len(selected_pos_samples), replace=True).tolist()
sel_samples = selected_pos_samples + selected_neg_samples
else:
# in the extreme case where num_rois = 1, we pick a random pos or neg sample
selected_pos_samples = pos_samples.tolist()
selected_neg_samples = neg_samples.tolist()
if np.random.randint(0, 2):
sel_samples = random.choice(neg_samples)
else:
sel_samples = random.choice(pos_samples)
if train_final_classifier:
loss_class = model_classifier.train_on_batch([X, X2[:, sel_samples, :]], [Y1[:, sel_samples, :], Y2[:, sel_samples, :]])
# losses
if train_rpn:
losses[iter_num, 0] = loss_rpn[1]
losses[iter_num, 1] = loss_rpn[2]
else:
losses[iter_num, 0] = 0
losses[iter_num, 1] = 0
if train_final_classifier:
losses[iter_num, 2] = loss_class[1]
losses[iter_num, 3] = loss_class[2]
losses[iter_num, 4] = loss_class[3] # accuracy
else:
losses[iter_num, 2] = 0
losses[iter_num, 3] = 0
losses[iter_num, 4] = 0
iter_num += 1
progbar.update(iter_num, [('rpn_cls', np.mean(losses[:iter_num, 0])), ('rpn_regr', np.mean(losses[:iter_num, 1])),
('detector_cls', np.mean(losses[:iter_num, 2])), ('detector_regr', np.mean(losses[:iter_num, 3]))])
if iter_num == epoch_length:
if train_rpn:
loss_rpn_cls = np.mean(losses[:, 0])
loss_rpn_regr = np.mean(losses[:, 1])
else:
loss_rpn_cls = 0
loss_rpn_regr = 0
if train_final_classifier:
loss_class_cls = np.mean(losses[:, 2])
loss_class_regr = np.mean(losses[:, 3])
class_acc = np.mean(losses[:, 4])
else:
loss_class_cls = 0
loss_class_regr = 0
class_acc = 0
mean_overlapping_bboxes = float(sum(rpn_accuracy_for_epoch)) / len(rpn_accuracy_for_epoch)
rpn_accuracy_for_epoch = []
if C.verbose:
print('Mean number of bounding boxes from RPN overlapping ground truth boxes: {}'.format(mean_overlapping_bboxes))
print('Classifier accuracy for bounding boxes from RPN: {}'.format(class_acc))
print('Loss RPN classifier: {}'.format(loss_rpn_cls))
print('Loss RPN regression: {}'.format(loss_rpn_regr))
print('Loss Detector classifier: {}'.format(loss_class_cls))
print('Loss Detector regression: {}'.format(loss_class_regr))
print('Elapsed time: {}'.format(time.time() - start_time))
loss_dict_train = {"rpn_cls":loss_rpn_cls,"rpn_reg":loss_rpn_regr,"final_cls":loss_class_cls,"final_reg":loss_class_regr}
curr_loss = 0
for l in losses_to_watch:
curr_loss += loss_dict_train[l]
iter_num = 0
start_time = time.time()
write_log(tbCallBack, train_names, [loss_rpn_cls,loss_rpn_regr,loss_class_cls,loss_class_regr,curr_loss,class_acc], epoch_num)
if curr_loss < best_loss:
if C.verbose:
print('Total loss decreased from {} to {} in training, saving weights'.format(best_loss,curr_loss))
save_log_data = '\nTotal loss decreased from {} to {} in epoch {}/{} in training, saving weights'.format(best_loss,curr_loss,epoch_num + 1,num_epochs)
with open("./saving_log.txt","a") as f:
f.write(save_log_data)
best_loss = curr_loss
model_all.save_weights(C.weights_all_path)
break
except Exception as e:
print('Exception: {}'.format(e))
continue
if validation_interval > 0:
# validation
if (epoch_num+1)%validation_interval==0 :
progbar = generic_utils.Progbar(validation_epoch_length)
print("Validation... \n")
while True:
try:
X, Y, img_data = next(data_gen_val)
if train_rpn:
val_loss_rpn = model_rpn.test_on_batch(X, Y)
P_rpn = model_rpn.predict_on_batch(X)
R = roi_helpers.rpn_to_roi(P_rpn[0], P_rpn[1], C, K.image_dim_ordering(), use_regr=True, overlap_thresh=C.rpn_nms_threshold,flag="train")
# note: calc_iou converts from (x1,y1,x2,y2) to (x,y,w,h) format
X2, Y1, Y2, IouS = roi_helpers.calc_iou(R, img_data, C, class_mapping)
neg_samples = np.where(Y1[0, :, -1] == 1)
pos_samples = np.where(Y1[0, :, -1] == 0)
if len(neg_samples) > 0:
neg_samples = neg_samples[0]
else:
neg_samples = []
if len(pos_samples) > 0:
pos_samples = pos_samples[0]
else:
pos_samples = []
rpn_accuracy_rpn_monitor.append(len(pos_samples))
rpn_accuracy_for_epoch.append((len(pos_samples)))
if C.num_rois > 1:
if len(pos_samples) < C.num_rois//2:
selected_pos_samples = pos_samples.tolist()
else:
selected_pos_samples = np.random.choice(pos_samples, C.num_rois//2, replace=False).tolist()
try:
selected_neg_samples = np.random.choice(neg_samples, C.num_rois - len(selected_pos_samples), replace=False).tolist()
except:
selected_neg_samples = np.random.choice(neg_samples, C.num_rois - len(selected_pos_samples), replace=True).tolist()
sel_samples = selected_pos_samples + selected_neg_samples
else:
# in the extreme case where num_rois = 1, we pick a random pos or neg sample
selected_pos_samples = pos_samples.tolist()
selected_neg_samples = neg_samples.tolist()
if np.random.randint(0, 2):
sel_samples = random.choice(neg_samples)
else:
sel_samples = random.choice(pos_samples)
if train_final_classifier:
val_loss_class = model_classifier.test_on_batch([X, X2[:, sel_samples, :]], [Y1[:, sel_samples, :], Y2[:, sel_samples, :]])
if train_rpn:
losses_val[iter_num, 0] = val_loss_rpn[1]
losses_val[iter_num, 1] = val_loss_rpn[2]
else:
losses_val[iter_num, 0] = 0
losses_val[iter_num, 1] = 0
if train_final_classifier:
losses_val[iter_num, 2] = val_loss_class[1]
losses_val[iter_num, 3] = val_loss_class[2]
losses_val[iter_num, 4] = val_loss_class[3]
else:
losses_val[iter_num, 2] = 0
losses_val[iter_num, 3] = 0
losses_val[iter_num, 4] = 0
iter_num += 1
progbar.update(iter_num, [('rpn_cls', np.mean(losses_val[:iter_num, 0])), ('rpn_regr', np.mean(losses_val[:iter_num, 1])),
('detector_cls', np.mean(losses_val[:iter_num, 2])), ('detector_regr', np.mean(losses_val[:iter_num, 3]))])
if iter_num == validation_epoch_length:
if train_rpn:
val_loss_rpn_cls = np.mean(losses_val[:, 0])
val_loss_rpn_regr = np.mean(losses_val[:, 1])
else:
val_loss_rpn_cls = 0
val_loss_rpn_regr = 0
if train_final_classifier:
val_loss_class_cls = np.mean(losses_val[:, 2])
val_loss_class_regr = np.mean(losses_val[:, 3])
val_class_acc = np.mean(losses_val[:, 4])
else:
val_loss_class_cls = 0
val_loss_class_regr = 0
val_class_acc = 0
mean_overlapping_bboxes = float(sum(rpn_accuracy_for_epoch)) / len(rpn_accuracy_for_epoch)
rpn_accuracy_for_epoch = []
loss_dict_valid = {"rpn_cls":val_loss_rpn_cls,"rpn_reg":val_loss_rpn_regr,"final_cls":val_loss_class_cls,"final_reg":val_loss_class_regr}
val_curr_loss = 0
for l in losses_to_watch:
val_curr_loss += loss_dict_valid[l]
write_log(tbCallBack, val_names, [val_loss_rpn_cls,val_loss_rpn_regr,val_loss_class_cls,val_loss_class_regr,val_curr_loss,val_class_acc], epoch_num)
if C.verbose:
print('[INFO VALIDATION]')
print('Mean number of bounding boxes from RPN overlapping ground truth boxes: {}'.format(mean_overlapping_bboxes))
print('Classifier accuracy for bounding boxes from RPN: {}'.format(val_class_acc))
print('Loss RPN classifier: {}'.format(val_loss_rpn_cls))
print('Loss RPN regression: {}'.format(val_loss_rpn_regr))
print('Loss Detector classifier: {}'.format(val_loss_class_cls))
print('Loss Detector regression: {}'.format(val_loss_class_regr))
print("current loss: %.2f, best loss: %.2f at epoch: %d"%(val_curr_loss,val_best_loss,val_best_loss_epoch))
print('Elapsed time: {}'.format(time.time() - start_time))
if val_curr_loss < val_best_loss:
if C.verbose:
print('Total loss decreased from {} to {}, saving weights'.format(val_best_loss,val_curr_loss))
save_log_data = '\nTotal loss decreased from {} to {} in epoch {}/{} in validation, saving weights'.format(val_best_loss,val_curr_loss,epoch_num + 1 ,num_epochs)
with open("./saving_log.txt","a") as f:
f.write(save_log_data)
val_best_loss = val_curr_loss
val_best_loss_epoch=epoch_num
model_all.save_weights(C.weights_all_path)
start_time = time.time()
iter_num = 0
break
except:
pass
print('Training complete, exiting.')